title: Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties.
abstract: Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.
authors.forename: Nadine
authors.initials: N
authors.lastname: Castelhano Santos
authors.forename: Maria Olívia
authors.initials: MO
authors.lastname: Pereira
authors.forename: Anália
authors.initials: A
authors.lastname: Lourenço
mesh_terms: D018441:Biofilms; D002176:Candida albicans / Q000472:pathogenicity; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D006801:Humans; D008954:Models, Biological; D011550:Pseudomonas aeruginosa / Q000472:pathogenicity; D013211:Staphylococcus aureus / Q000472:pathogenicity; D014774:Virulence
keywords: biofilm-associated infections; drug therapeutics; in silico tools; network reconstruction; pathogenicity